


import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class        __lowercase       (							unittest.TestCase  ):



		"""simple docstring"""

		@property
		def 					__A   ( self	)  -> List[str]:







					'''simple docstring'''







					torch.manual_seed(0	)
					lowerCamelCase    		=  UNetaDModel(
					    block_out_channels=(32, 64)		,      layers_per_block=2		,      sample_size=32		,      in_channels=3		,      out_channels=3		,      down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")		,      up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")		,      )
					return model







		def 					__A   ( self	)  -> List[Any]:







					'''simple docstring'''







					lowerCamelCase    		=  self.dummy_uncond_unet
					lowerCamelCase    		=  ScoreSdeVeScheduler()

					lowerCamelCase    		=  ScoreSdeVePipeline(unet=A		,      scheduler=A	)
					sde_ve.to(A	)
					sde_ve.set_progress_bar_config(disable=A	)

					lowerCamelCase    		=  torch.manual_seed(0	)
					lowerCamelCase    		=  sde_ve(num_inference_steps=2		,      output_type="""numpy"""		,      generator=A	).images

					lowerCamelCase    		=  torch.manual_seed(0	)
					lowerCamelCase    		=  sde_ve(num_inference_steps=2		,      output_type="""numpy"""		,      generator=A		,      return_dict=A	)[
					    0
					]

					lowerCamelCase    		=  image[0, -3:, -3:, -1]
					lowerCamelCase    		=  image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					lowerCamelCase    		=  np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]	)

					assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2







@slow
@require_torch
class        __lowercase       (							unittest.TestCase  ):



		"""simple docstring"""

		def 					__A   ( self	)  -> Optional[int]:







					'''simple docstring'''







					lowerCamelCase    		=  """google/ncsnpp-church-256"""
					lowerCamelCase    		=  UNetaDModel.from_pretrained(A	)

					lowerCamelCase    		=  ScoreSdeVeScheduler.from_pretrained(A	)

					lowerCamelCase    		=  ScoreSdeVePipeline(unet=A		,      scheduler=A	)
					sde_ve.to(A	)
					sde_ve.set_progress_bar_config(disable=A	)

					lowerCamelCase    		=  torch.manual_seed(0	)
					lowerCamelCase    		=  sde_ve(num_inference_steps=10		,      output_type="""numpy"""		,      generator=A	).images

					lowerCamelCase    		=  image[0, -3:, -3:, -1]

					assert image.shape == (1, 2_56, 2_56, 3)

					lowerCamelCase    		=  np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]	)

					assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2








"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  {
    "facebook/s2t-wav2vec2-large-en-de": (
        "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}

class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = 'speech_to_text_2'
      _SCREAMING_SNAKE_CASE							      = ['past_key_values']
      _SCREAMING_SNAKE_CASE							      = {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}





      def __init__(			self ,		lowercase=10_000 ,		lowercase=6 ,		lowercase=2_048 ,		lowercase=4 ,		lowercase=0.0 ,		lowercase=True ,		lowercase="relu" ,		lowercase=256 ,		lowercase=0.1 ,		lowercase=0.0 ,		lowercase=0.0 ,		lowercase=0.02 ,		lowercase=2 ,		lowercase=True ,		lowercase=1 ,		lowercase=0 ,		lowercase=2 ,		lowercase=1_024 ,		**lowercase ,		)						->	Optional[int]:
            lowerCAmelCase    		=			vocab_size
            lowerCAmelCase    		=			d_model
            lowerCAmelCase    		=			decoder_ffn_dim
            lowerCAmelCase    		=			decoder_layers
            lowerCAmelCase    		=			decoder_attention_heads
            lowerCAmelCase    		=			dropout
            lowerCAmelCase    		=			attention_dropout
            lowerCAmelCase    		=			activation_dropout
            lowerCAmelCase    		=			activation_function
            lowerCAmelCase    		=			init_std
            lowerCAmelCase    		=			decoder_layerdrop
            lowerCAmelCase    		=			use_cache
            lowerCAmelCase    		=			decoder_layers
            lowerCAmelCase    		=			scale_embedding  # scale factor will be sqrt(d_model) if True
            lowerCAmelCase    		=			max_target_positions

            super().__init__(
                pad_token_id=lowercase ,		bos_token_id=lowercase ,		eos_token_id=lowercase ,		decoder_start_token_id=lowercase ,		**lowercase ,		)






